A method and device for evaluating V2G capability based on adaptive reallocation

Abstract

A transfer V2G capability evaluation method and device based on adaptive redistribution, the method determines the transfer instructions of V2G sites by constructing a traffic network model, and completes the initialization of transfer travel demand; updates the transfer travel demand matrix according to the matched transfer origin-dispatch destination pair and the corresponding number of transfer EVs; constructs a transfer V2G redistribution model to formulate travel routes and shortest travel times for transfer EVs, and evaluates the on-time transfer V2G capability of setting the transfer time; adaptively reduces the number of transfer EVs scheduled for late transfer origins according to the obtained transfer EV reduction rate, and updates the transfer travel demand matrix; performs transfer V2G redistribution and evaluates the on-time transfer V2G capability through iterative transfer V2G redistribution model, and obtains enhanced on-time transfer V2G capability. The present invention can construct on-time V2G scheduling and enhancement circles with each dispatching V2G site as the center and limited transfer time as the constraint, which is helpful to formulate accurate and efficient V2G emergency scheduling strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of V2G capability analysis, and in particular to a method and device for evaluating transfer V2G capability based on adaptive reallocation. Background Art

[0002] The growing number of electric vehicles (EVs) has strengthened the coupling of power and transportation systems, while also bringing huge new mobile energy storage potential. Since 2009, more than 140 research projects have been carried out in 27 countries to verify the feasibility of utilizing EV energy storage potential through vehicle-to-grid (V2G) technology. At the same time, many researchers are working hard to explore how to more effectively utilize V2G capabilities to support efficient and reliable operation of power systems. However, EVs must rely on V2G facilities that are located in a defined position and support bidirectional power flow to supply power to the grid. In addition, unlike fixed or controllable energy storage devices or distributed generation equipment, EVs usually prioritize their own travel plans and are randomly distributed in the transportation network. The mismatch between the location of V2G facilities and the distribution of idle EV resources may result in insufficient V2G support during emergency load shortages. Therefore, evaluating and improving the actual V2G capacity at the facilities is crucial to improving emergency V2G support.

[0003] At present, the number of EVs that can participate in V2G at each V2G site under the condition of unchanged discharge power represents its local V2G capacity. Many studies have explored how to use local V2G capacity to enhance the daily operation of the power system. In addition, in order to make full use of EV energy storage resources, some studies aim to determine the evaluation and incentive measures to maximize the local V2G capacity. However, compared with the charging behavior necessary for daily energy replenishment, the frequency of EV users participating in V2G is relatively low. Therefore, after weighing factors such as existing facilities, technology / conversion costs, and market orientation, not all charging points can support V2G in the foreseeable future. As a result, a large number of EV resources may be idle for a long time at locations other than V2G sites. In particular, the available dischargeable EVs near V2G sites may still be insufficient to meet the emergency restoration needs when large-scale grid load is lost. Therefore, solving the mismatch between supply and demand of V2G resources has become the key to improving the V2G grid connection capacity in emergency situations.

[0004] Further considering the flexible mobility of EVs, in emergency situations where the local V2G capacity of some normally operating V2G sites is insufficient, idle EVs at nearby locations can be urgently dispatched to provide additional V2G support. The number of dispatchable transfer EVs reflects the transfer V2G capacity of the corresponding V2G sites. However, although existing studies have considered transfer V2G scheduling, they have ignored the conflict between the actual transfer time consumed by EVs in the traffic network and the timeliness required by the short-term emergency scheduling strategy on the grid side. This oversight means that existing studies cannot ensure the V2G capacity of timely access during actual scheduling, which seriously affects the actual implementation of related strategies. Therefore, it is necessary to evaluate the on-time transfer V2G capacity within a limited time to ensure the accurate formulation and effective implementation of the transfer V2G scheduling strategy.

[0005] In addition to basic traffic parameters such as topology and road length, EV transfer time is also affected by traffic operation status. Therefore, existing studies have considered the interaction between transfer EV trips in the traffic system and evaluated the EV scheduling potential under the influence of transfer traffic conditions. However, this study assumes that all idle EVs are scheduled at the same time, resulting in traffic congestion caused by numerous transfer EVs. This congestion will eventually delay the integration of transfer EVs with the grid and reduce the overall responsiveness. Therefore, there is an urgent need for an effective method to alleviate the traffic congestion that may occur with the increase in the number of transfer EVs. In addition, the traffic system also accommodates a large number of regular vehicle trips. During EV transfer scheduling, the traffic state includes the basic state and the transfer state. More importantly, in order to access V2G as soon as possible, emergency transfer EVs usually give priority to route planning with the shortest travel time. However, classic traffic impedance models (such as the Highway Administration model) show that the growth rate of travel time tends to surge with the increase of traffic states (such as saturation states). Therefore, in the process of ensuring the timely arrival and grid connection of transfer EVs, the impact of the basic traffic state formed by a large number of regular trips cannot be ignored. Although the flexible mobility of EVs provides a prerequisite for emergency dispatch, to improve on-time V2G support in emergency situations, it is also necessary to solve the V2G resource supply and demand mismatch and traffic congestion problems under the dual influence of basic and transfer traffic states.

[0006] Therefore, designing a transfer V2G capacity assessment method for adaptive reallocation to solve the V2G resource supply and demand mismatch and traffic congestion problems under the dual influence of basic and transfer traffic conditions has become an urgent problem to be solved. Summary of the invention

[0007] To this end, the present invention provides a transfer V2G capability assessment method and device based on adaptive reallocation. The V2G capability assessment and enhancement method based on adaptive reallocation can construct a punctual V2G scheduling and enhancement circle centered on each scheduling V2G site and constrained by a limited transfer duration, which is helpful to formulate an accurate and efficient V2G emergency scheduling strategy.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a transfer V2G capability evaluation method based on adaptive reallocation, comprising:

[0009] Constructing a traffic network model, determining the transfer instructions of the V2G site through the traffic network model, and matching the transfer starting point with a number of V2G dispatch destinations to complete the transfer travel demand initialization;

[0010] According to the matched transfer origin-dispatch destination pairs and the corresponding number of transferred EVs, a transfer travel demand matrix is ​​updated; a transfer V2G reallocation model is constructed, travel routes and shortest travel times are determined for the transferred EVs through the transfer V2G reallocation model, and the on-time transfer V2G capability of the set transfer duration is evaluated;

[0011] Calculate the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index, and obtain the reduction rate of the transfer EV corresponding to the late transfer starting place; adaptively reduce the number of transfer EVs scheduled at the late transfer starting place according to the obtained reduction rate of the transfer EV, and update the transfer travel demand matrix;

[0012] According to the updated transfer travel demand matrix, the transfer V2G reallocation is iteratively performed through the transfer V2G reallocation model and the on-time transfer V2G capability is evaluated until the set requirements are met, and the iterative evaluation is stopped to obtain the enhanced on-time transfer V2G capability.

[0013] As a preferred solution of the transfer V2G capability evaluation method based on adaptive reallocation, the transfer starting point is matched with a number of V2G scheduling destinations through the traffic network model, and the expression for completing the transfer travel demand initialization is:

[0014]

[0015]

[0016] In the formula, is the limited transfer duration; N is the node set; are the traffic node sets that operate normally during period t and those that need additional V2G transfer; is the shortest travel time from m to n when only basic traffic conditions are considered; n′ is the dispatch destination node with the shortest travel time starting from m; sm is the number of EVs that can be dispatched at m; q mn′ is the updated transfer travel demand from m to n′.

[0017] As a preferred solution of a transfer V2G capability evaluation method based on adaptive reallocation, in the process of constructing a transfer V2G reallocation model, the expression of the transfer V2G reallocation model for each scheduling period t is:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] In the formula, min z represents the minimized total regional transfer time z; is the total section transfer time integral for the area; is the total intersection turn transfer time integral for the area; and are the transfer flows on the road segment (i, j) and the turn (i, j, l) respectively; and are the corresponding basic flows respectively; and are the transfer time impedances of (i, j) and (i, j, l), respectively, and are given by the model Γ BPR (·) and Γ WEB (·) calculated; Indicates that the kth path between the transfer origin-destination pair (m,n) passes through (i,j) or (i,j,l), otherwise it is 0; and are the transfer flow and time of the path respectively; q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix.

[0028] As a preferred solution of a transfer V2G capability evaluation method based on adaptive reallocation, in the process of evaluating the on-time transfer V2G capability of setting the transfer duration through the transfer V2G reallocation model, the on-time transfer V2G capability expression is:

[0029]

[0030]

[0031]

[0032] Where TC n is the on-time transfer V2G capability of the corresponding node n; q′ mn for The number of EVs that can reach n from m within q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; is the transfer time from m to n obtained by the V2G reallocation model; To limit the transfer time; E' mn is the corresponding EV set; e is the sequence number of EV; S min Minimum SOC threshold to prevent over-discharge; The remaining SOC of the e-th transferred EV after reaching destination n and participating in V2G; for Whether it complies with battery safety requirements, 1 or 0 represents compliance or non-compliance respectively.

[0033] As a preferred solution of a transfer V2G capability evaluation method based on adaptive reallocation, in the process of calculating the contribution or sensitivity of a late transfer starting point to transfer congestion according to a transfer congestion index and obtaining a reduction rate of transfer EVs corresponding to the late transfer starting point, the expression of the transfer congestion index is:

[0034]

[0035] In the formula, θ mn is the transfer congestion index of m among all late origins of the dispatch destination n; Any transfer starting point node; for Can be transferred from any starting point node within The number of EVs that reach n; To transfer the V2G reallocation model from The transfer time to reach n; To shift the EV travel demand matrix The number of transferred EVs.

[0036] The present invention also provides a transfer V2G capability assessment device based on adaptive reallocation, based on the above transfer V2G capability assessment method based on adaptive reallocation, comprising:

[0037] A traffic network model construction and processing module is used to construct a traffic network model, determine the transfer instructions of the V2G site through the traffic network model, and match the transfer starting point with a number of V2G scheduling destinations to complete the transfer travel demand initialization;

[0038] A just-in-time transfer V2G capability evaluation module is used to update the transfer travel demand matrix according to the matched transfer origin-dispatch destination pairs and the corresponding number of transfer EVs; construct a transfer V2G reallocation model, formulate travel routes and shortest travel times for transfer EVs through the transfer V2G reallocation model, and evaluate the just-in-time transfer V2G capability for a set transfer duration;

[0039] A transfer EV scheduling quantity reduction module is used to calculate the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index, and obtain the reduction rate of the transfer EV corresponding to the late transfer starting place; according to the obtained transfer EV reduction rate, adaptively reduce the transfer EV scheduling quantity of the late transfer starting place, and update the transfer travel demand matrix;

[0040] The on-time transfer V2G capability iterative evaluation module is used to iteratively perform transfer V2G reallocation and evaluate the on-time transfer V2G capability according to the updated transfer travel demand matrix through the transfer V2G reallocation model until the set requirements are met, stop the iterative evaluation, and obtain the enhanced on-time transfer V2G capability.

[0041] As a preferred solution of a transfer V2G capability evaluation device based on adaptive reallocation, in the traffic network model construction and application module, the transfer starting point is matched with a plurality of V2G scheduling destinations through the traffic network model, and the expression for completing the transfer travel demand initialization is:

[0042]

[0043]

[0044] In the formula, is the limited transfer duration; N is the node set; are the traffic node sets that operate normally during period t and those that need additional V2G transfer; is the shortest travel time from m to n when only basic traffic conditions are considered; n′ is the dispatch destination node with the shortest travel time starting from m; s m is the number of EVs that can be dispatched at m; q mn′is the updated transfer travel demand from m to n′.

[0045] As a preferred solution of a transfer V2G capability evaluation device based on adaptive reallocation, in the on-time transfer V2G capability evaluation module, in the process of constructing a transfer V2G reallocation model, the transfer V2G reallocation model expression of each scheduling period t is:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] In the formula, min z represents the minimized total regional transfer time z; is the total section transfer time integral for the area; is the total intersection turn transfer time integral for the area; and are the transfer flows on the road segment (i, j) and the turn (i, j, l) respectively; and are the corresponding basic flows respectively; and are the transfer time impedances of (i, j) and (i, j, l), respectively, and are given by the model Γ BPR (·) and Γ WEB (·) calculated; Indicates that the kth path between the transfer origin-destination pair (m,n) passes through (i,j) or (i,j,l), otherwise it is 0; and are the transfer flow and time of the path respectively; q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix.

[0056] As a preferred solution of a transfer V2G capability evaluation device based on adaptive reallocation, in the on-time transfer V2G capability evaluation module, in the process of evaluating the on-time transfer V2G capability of setting the transfer duration through the transfer V2G reallocation model, the on-time transfer V2G capability expression is:

[0057]

[0058]

[0059]

[0060] Where TC n is the on-time transfer V2G capability of the corresponding node n; q′ mn for The number of EVs that can reach n from m within q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; is the transfer time from m to n obtained by the V2G reallocation model; To limit the transfer time; E' mn is the corresponding EV set; e is the sequence number of EV; S min Minimum SOC threshold to prevent over-discharge; The remaining SOC of the e-th transferred EV after reaching destination n and participating in V2G; for Whether it complies with battery safety requirements, 1 or 0 represents compliance or non-compliance respectively.

[0061] As a preferred solution of a transfer V2G capability evaluation device based on adaptive reallocation, in the transfer EV scheduling quantity reduction module, in the process of calculating the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index and obtaining the reduction rate of the transfer EV corresponding to the late transfer starting place, the expression of the transfer congestion index is:

[0062]

[0063] In the formula, θ mn is the transfer congestion index of m among all late origins of the dispatch destination n; Any transfer starting point node; for Can be transferred from any starting point node within The number of EVs that reach n; To transfer the V2G reallocation model from The transfer time to reach n; To shift the EV travel demand matrix The number of transferred EVs.

[0064] The present invention has the following advantages: constructing a traffic network model, determining the transfer instructions of the V2G site through the traffic network model, and matching the transfer starting place with a number of V2G scheduling destinations to complete the initialization of the transfer travel demand; updating the transfer travel demand matrix according to the matched transfer starting place-scheduling destination pairs and the corresponding number of transfer EVs; constructing a transfer V2G reallocation model, formulating travel routes and shortest driving times for transfer EVs through the transfer V2G reallocation model, and evaluating the on-time transfer V2G capability of the set transfer duration; calculating the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index, and obtaining the reduction rate of the transfer EV corresponding to the late transfer starting place; adaptively reducing the number of transfer EVs scheduled at the late transfer starting place according to the obtained reduction rate of the transfer EV, and updating the transfer travel demand matrix; according to the updated transfer travel demand matrix, iteratively performing transfer V2G reallocation and evaluating the on-time transfer V2G capability through the transfer V2G reallocation model until the set requirements are met, stopping the iterative evaluation, and obtaining an enhanced on-time transfer V2G capability. The present invention can obtain the shortest travel time and route of all given transfer EVs through the constructed transfer V2G reallocation model to evaluate the on-time transfer V2G capability with a limited transfer time. At the same time, the proposed adaptive enhancement (ADE) algorithm based on basic traffic matching (BTM) and transfer congestion index (TCI) can maximize the number of EVs that can be scheduled for transfer at the same time to enhance the on-time transfer V2G capability. Among them, the BTM model matches the transfer starting point with multiple V2G scheduling destinations under basic traffic conditions for the initialization of transfer travel demand. TCI quantifies the contribution or sensitivity of all late transfer starting points to transfer congestion to determine the reduction ratio of the corresponding transfer EV when updating the transfer travel demand. The overall V2G capability evaluation and enhancement method based on adaptive reallocation of the present invention can construct an on-time V2G scheduling and enhancement circle centered on each scheduling V2G site and constrained by a limited transfer time, which is helpful to formulate accurate and efficient V2G emergency scheduling strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0066] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0067] Figure 1 A schematic flow chart of a method for evaluating V2G transfer capability based on adaptive reallocation provided in Embodiment 1 of the present invention;

[0068] Figure 2 This is a schematic diagram of the architecture of a transfer V2G capability assessment device based on adaptive reallocation provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0069] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] Example 1

[0071] See also Figure 1 Embodiment 1 of the present invention provides a method for evaluating V2G transfer capability based on adaptive reallocation, comprising the following steps:

[0072] S1. Construct a traffic network model, determine the transfer instructions of the V2G site through the traffic network model, and match the transfer starting point with several V2G scheduling destinations to complete the transfer travel demand initialization;

[0073] S2. Update the transfer travel demand matrix according to the matched transfer origin-dispatch destination pairs and the corresponding number of transferred EVs; construct a transfer V2G reallocation model, formulate travel routes and shortest travel times for the transferred EVs through the transfer V2G reallocation model, and evaluate the on-time transfer V2G capability of the set transfer duration;

[0074] S3, calculating the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index, and obtaining the reduction rate of the transfer EV corresponding to the late transfer starting place; adaptively reducing the number of transfer EVs dispatched at the late transfer starting place according to the obtained reduction rate of the transfer EV, and updating the transfer travel demand matrix;

[0075] S4. According to the updated transfer travel demand matrix, the transfer V2G reallocation model is used to iteratively perform transfer V2G reallocation and evaluate the on-time transfer V2G capability until the set requirements are met, and the iterative evaluation is stopped to obtain an enhanced on-time transfer V2G capability.

[0076] In this embodiment, in step S1, a traffic network model is constructed, a transfer instruction of a V2G station is determined through the traffic network model, and a transfer starting point is matched with a plurality of V2G scheduling destinations to complete the initialization of the transfer travel demand;

[0077] Specifically, assume that the traffic network is a directed graph G(N,A,T), where N, A, and T are the node set, link set, and intersection turn set, respectively. The link from node i∈N to node j∈N is represented by (i,j)∈A. The turn from link (i,j)∈A to (j,l)∈A is represented by (i,j,l)∈T. Then, the distribution and state expressions of V2G facilities are set as:

[0078]

[0079]

[0080] Where N V represents the set of traffic nodes containing V2G facilities; represents the set of traffic nodes that operate normally during period t; σ v , They represent the V2G facility existence instruction and operation instruction of node v (in time period t), and 1 represents the corresponding positive value.

[0081] When the local V2G capacity of some normally operating V2G sites is insufficient, an emergency transfer instruction can be issued to call idle EVs in other places other than the normally operating V2G sites to provide additional transfer V2G capacity. The expression of the emergency transfer instruction is:

[0082]

[0083] In the formula, represents the set of traffic nodes that need additional V2G transfers during period t; Represents the V2G facility transfer instruction of node v (in time period t), and 1 represents the corresponding positive value.

[0084] The travel time of the transfer EV depends on the basic traffic conditions and the transfer traffic conditions. Determining the initial transfer travel demand, that is, initializing the resource matching between the transfer starting point and the dispatch destination, is a prerequisite for evaluating and enhancing the transfer V2G capability. In particular, when multiple V2G stations issue emergency transfer instructions at the same time, reasonable resource matching is more necessary. To this end, the basic traffic matching (BTM) model is used to initialize the resource matching between the transfer starting point and multiple V2G dispatch destinations.

[0085] By matching the transfer starting point with several V2G dispatch destinations through the traffic network model, the expression for completing the transfer travel demand initialization is:

[0086]

[0087]

[0088] In the formula, is the limited transfer duration; N is the node set; are the traffic node sets that operate normally during period t and those that need additional V2G transfer; is the shortest travel time from m to n when only basic traffic conditions are considered; n′ is the dispatch destination node with the shortest travel time starting from m; s m is the number of EVs that can be dispatched at m; q mn′ is the updated transfer travel demand from m to n′. For simplicity, the time period t is omitted here and in the following formulas unless otherwise specified.

[0089] In this embodiment, in step S2, the transfer travel demand matrix is ​​updated according to the matched transfer origin-dispatch destination pairs and the corresponding number of transferred EVs; a transfer V2G reallocation model is constructed, and the travel route and the shortest driving time are formulated for the transfer EVs through the transfer V2G reallocation model, and the on-time transfer V2G capability of the set transfer duration is evaluated.

[0090] Specifically, in order to reach the dispatch destination as quickly as possible, the route planning of the transfer EV usually gives priority to the shortest travel time. However, the arrival time of the transfer EV is affected by the basic traffic flow (composed of all regular vehicles) and the transfer traffic flow (composed of the transfer EV) in the traffic network. In addition, this route allocation needs to comply with the Wardrop first equilibrium principle to ensure fairness and stability, ensuring that any transfer EV cannot unilaterally reduce its travel time by changing the route.

[0091] Therefore, from the perspective of traffic operation, the transfer V2G routing problem essentially involves the redistribution of the transfer EV travel demand superimposed on the basic traffic state. To this end, assuming that the power grid can obtain accurate traffic status and the transfer routes of EVs willing to participate are planned uniformly by the power grid, the transfer V2G reallocation model expression for each scheduling period t is:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] In the formula, min z represents the minimized total regional transfer time z; is the total section transfer time integral for the area; is the total intersection turn transfer time integral for the area; and are the transfer flows on the road segment (i, j) and the turn (i, j, l) respectively; and are the corresponding basic flows respectively; and are the transfer time impedances of (i, j) and (i, j, l), respectively, and are given by the model Γ BPR (·) and Γ WEB (·) calculated; Indicates that the kth path between the transfer origin-destination pair (m,n) passes through (i,j) or (i,j,l), otherwise it is 0; and are the transfer flow and time of the path respectively; q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix.

[0102] Solving the transfer V2G redistribution model can determine the shortest travel time and path for all transferred EVs.

[0103] EVs will inevitably consume battery state of charge (SOC) during the transfer process. Therefore, when evaluating the transfer V2G capability, it is necessary not only to determine the number of transfer EVs that can arrive within a limited time, but also to ensure that the transfer EVs evaluated still have enough remaining SOC to participate in V2G after arriving at the corresponding dispatch destination.

[0104] Therefore, based on the shortest routing time of the EV after reallocation and considering the transfer SOC consumption constraint, the on-time transfer V2G capability expression is used to evaluate the ability to transfer EVs within a limited time. The number of EVs that arrive on time within the time frame represents the on-time transfer V2G capability TC of the corresponding node n. n The on-time transfer V2G capability expression is:

[0105]

[0106]

[0107]

[0108] Where TC n is the on-time transfer V2G capability of the corresponding node n; q′ mn for The number of EVs that can reach n from m within q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; is the transfer time from m to n obtained by the V2G reallocation model; is to limit the transfer time; E′mn is the corresponding EV set; e is the sequence number of EV; Smin is the minimum SOC threshold to prevent over-discharge; The remaining SOC of the e-th transferred EV after reaching destination n and participating in V2G; for Whether it complies with battery safety requirements, 1 or 0 represents compliance or non-compliance respectively.

[0109] In this embodiment, in step S3, the contribution or sensitivity of the late transfer starting point to the transfer congestion is calculated according to the transfer congestion index, and the reduction rate of the transfer EV corresponding to the late transfer starting point is obtained; according to the obtained reduction rate of the transfer EV, the number of transfer EVs scheduled at the late transfer starting point is adaptively reduced, and the transfer travel demand matrix is ​​updated.

[0110] Specifically, the additional congestion caused by the transfer EV is the main reason for the reduction in the on-time transfer V2G capacity. To effectively reduce the impact of transfer congestion, it is first necessary to determine which late transfer origins are prone to congestion or are particularly susceptible to congestion.

[0111] Transfer time and number are two key factors that cause EV transfer delays. In particular, the longer the transfer time, the more likely the EV transfer at the origin will exceed the limit, while the greater the number of transfers, the more likely it is to cause additional transfer congestion, thus delaying all related transfer EVs.

[0112] To this end, a transfer congestion index (TCI) is used to quantitatively evaluate the contribution or sensitivity of all late transfer origins to the corresponding destination transfer congestion. The expression of the transfer congestion index is:

[0113]

[0114] Where θmn is the transfer congestion index of m among all late origins of the dispatch destination n; Any transfer starting point node; for Can be transferred from any starting point node within The number of EVs that reach n; To transfer the V2G reallocation model from The transfer time to reach n; To shift the EV travel demand matrix The number of transferred EVs.

[0115] θ mn The larger the value, the greater the impact of the origin on transfer congestion or the more likely it is to be delayed due to congestion, and the more it is necessary to reduce the number of transfer EVs from the origin.

[0116] The scheduling number of late transfer EVs from each starting point is adaptively reduced by the reduction number calculation formula, and the reduction number calculation formula is:

[0117] θ mn : =min{θ mn ,θ max}

[0118]

[0119] In the formula, θ max Represents the maximum reduction step; := means updating the variable on the left with the value on the right.

[0120] In this embodiment, in step S4, according to the updated transfer travel demand matrix, the transfer V2G reallocation model is iteratively used to perform transfer V2G reallocation and evaluate the on-time transfer V2G capability until the set requirements are met, and the iterative evaluation is stopped to obtain the enhanced on-time transfer V2G capability.

[0121] Among them, until the set requirements are met means until all scheduled transfer EVs arrive on time or the maximum number of iterations is reached.

[0122] In summary, the present invention constructs a traffic network model, determines the transfer instructions of the V2G site through the traffic network model, matches the transfer starting point with several V2G scheduling destinations, and completes the initialization of the transfer travel demand; updates the transfer travel demand matrix according to the matched transfer starting point-scheduling destination pairs and the corresponding number of transfer EVs; constructs a transfer V2G reallocation model, formulates travel routes and shortest driving times for transfer EVs through the transfer V2G reallocation model, and evaluates the on-time transfer V2G capability of the set transfer duration; calculates the contribution or sensitivity of the late transfer starting point to the transfer congestion according to the transfer congestion index, and obtains the reduction rate of the transfer EV corresponding to the late transfer starting point; adaptively reduces the number of transfer EVs scheduled at the late transfer starting point according to the obtained reduction rate of the transfer EV, and updates the transfer travel demand matrix; according to the updated transfer travel demand matrix, iteratively performs transfer V2G reallocation and evaluates the on-time transfer V2G capability through the transfer V2G reallocation model until the set requirements are met, stops the iterative evaluation, and obtains the enhanced on-time transfer V2G capability. The present invention can obtain the shortest travel time and route of all given transfer EVs through the constructed transfer V2G reallocation model to evaluate the on-time transfer V2G capability with a limited transfer time. At the same time, the proposed adaptive enhancement (ADE) algorithm based on basic traffic matching (BTM) and transfer congestion index (TCI) can maximize the number of EVs that can be scheduled for transfer at the same time to enhance the on-time transfer V2G capability. Among them, the BTM model matches the transfer starting point with multiple V2G scheduling destinations under basic traffic conditions for the initialization of transfer travel demand. TCI quantifies the contribution or sensitivity of all late transfer starting points to transfer congestion to determine the reduction ratio of the corresponding transfer EV when updating the transfer travel demand. The overall V2G capability evaluation and enhancement method based on adaptive reallocation of the present invention can construct an on-time V2G scheduling and enhancement circle centered on each scheduling V2G site and constrained by a limited transfer time, which is helpful to formulate accurate and efficient V2G emergency scheduling strategies.

[0123] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0124] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] Example 2

[0126] See also Figure 2 Embodiment 2 of the present invention further provides a transfer V2G capability evaluation device based on adaptive reallocation, comprising:

[0127] Traffic network model building and processing module 001, used to build a traffic network model, determine the transfer instructions of the V2G site through the traffic network model, and match the transfer starting point with a number of V2G scheduling destinations to complete the transfer travel demand initialization;

[0128] The on-time transfer V2G capability evaluation module 002 is used to update the transfer travel demand matrix according to the matched transfer origin-dispatch destination pairs and the corresponding number of transfer EVs; construct a transfer V2G reallocation model, formulate travel routes and shortest travel times for transfer EVs through the transfer V2G reallocation model, and evaluate the on-time transfer V2G capability of the set transfer duration;

[0129] The transfer EV scheduling quantity reduction module 003 is used to calculate the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index, and obtain the reduction rate of the transfer EV corresponding to the late transfer starting place; according to the obtained transfer EV reduction rate, the transfer EV scheduling quantity of the late transfer starting place is adaptively reduced, and the transfer travel demand matrix is ​​updated;

[0130] The on-time transfer V2G capability iterative evaluation module 004 is used to iteratively perform transfer V2G reallocation and evaluate the on-time transfer V2G capability according to the updated transfer travel demand matrix through the transfer V2G reallocation model until the set requirements are met, stop the iterative evaluation, and obtain the enhanced on-time transfer V2G capability.

[0131] In this embodiment, in the traffic network model construction and application module 001, the expression for completing the transfer travel demand initialization is as follows:

[0132]

[0133]

[0134] In the formula, is the limited transfer duration; N is the node set; are the traffic node sets that operate normally during period t and those that need additional V2G transfer; is the shortest travel time from m to n when only basic traffic conditions are considered; n′ is the dispatch destination node with the shortest travel time starting from m; s m is the number of EVs that can be dispatched at m; q mn′ is the updated transfer travel demand from m to n′.

[0135] In this embodiment, in the on-time transfer V2G capability evaluation module 002, in the process of constructing the transfer V2G reallocation model, the transfer V2G reallocation model expression of each scheduling period t is:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] In the formula, min z represents the minimized total regional transfer time z; is the total section transfer time integral for the area; is the total intersection turn transfer time integral for the area; and are the transfer flows on the road segment (i, j) and the turn (i, j, l) respectively; and are the corresponding basic flows respectively; and are the transfer time impedances of (i, j) and (i, j, l), respectively, and are given by the model Γ BPR (·) and Γ WEB (·) calculated; Indicates that the kth path between the transfer origin-destination pair (m,n) passes through (i,j) or (i,j,l), otherwise it is 0; and are the transfer flow and time of the path respectively; q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix.

[0146] In this embodiment, in the on-time transfer V2G capability evaluation module 002, in the process of evaluating the on-time transfer V2G capability of setting the transfer duration through the transfer V2G reallocation model, the on-time transfer V2G capability expression is:

[0147]

[0148]

[0149]

[0150] Where TC n is the on-time transfer V2G capability of the corresponding node n; q′ mn for The number of EVs that can reach n from m within q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; is the transfer time from m to n obtained by the V2G reallocation model; To limit the transfer time; E' mn is the corresponding EV set; e is the sequence number of EV; S min Minimum SOC threshold to prevent over-discharge; The remaining SOC of the e-th transferred EV after reaching destination n and participating in V2G; for Whether it complies with battery safety requirements, 1 or 0 represents compliance or non-compliance respectively.

[0151] In this embodiment, in the transfer EV scheduling quantity reduction module 003, in the process of calculating the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index and obtaining the reduction rate of the transfer EV corresponding to the late transfer starting place, the expression of the transfer congestion index is:

[0152]

[0153] In the formula, θ mn is the transfer congestion index of m among all late origins of the dispatch destination n; Any transfer starting point node; for Can be transferred from any starting point node within The number of EVs that reach n; To transfer the V2G reallocation model from The transfer time to reach n; To shift the EV travel demand matrix The number of transferred EVs.

[0154] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0155] Example 3

[0156] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code of a method for evaluating a transfer V2G capability based on adaptive reallocation is stored, and the program code includes instructions for executing a method for evaluating a transfer V2G capability based on adaptive reallocation of embodiment 1 or any possible implementation thereof.

[0157] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0158] Example 4

[0159] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0160] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a transfer V2G capability assessment method based on adaptive reallocation of embodiment 1 or any possible implementation thereof.

[0161] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0162] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0163] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0164] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for evaluating V2G transfer capability based on adaptive reallocation, characterized in that: include: Constructing a traffic network model, determining the transfer instructions of the V2G site through the traffic network model, and matching the transfer starting point with a number of V2G dispatch destinations to complete the transfer travel demand initialization; According to the matched transfer origin-dispatch destination pairs and the corresponding number of transferred EVs, a transfer travel demand matrix is ​​updated; a transfer V2G reallocation model is constructed, travel routes and shortest travel times are determined for the transferred EVs through the transfer V2G reallocation model, and the on-time transfer V2G capability of the set transfer duration is evaluated; Calculate the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index, and obtain the reduction rate of the transfer EV corresponding to the late transfer starting place; adaptively reduce the number of transfer EVs scheduled at the late transfer starting place according to the obtained reduction rate of the transfer EV, and update the transfer travel demand matrix; According to the updated transfer travel demand matrix, iteratively perform transfer V2G reallocation and evaluate the on-time transfer V2G capability through the transfer V2G reallocation model until the set requirements are met, stop the iterative evaluation, and obtain the enhanced on-time transfer V2G capability; By matching the transfer starting point with several V2G dispatch destinations through the traffic network model, the expression for completing the transfer travel demand initialization is: In the formula, l t It is to limit the transfer time; N is the node set; is the set of traffic nodes that operate normally during period t; is the set of traffic nodes that need additional V2G transfer during period t; is the shortest travel time from m to n when only basic traffic conditions are considered; n′ is the dispatch destination node with the shortest travel time starting from m; s m is the number of EVs that can be dispatched at m; q mn′ is the updated transfer travel demand from m to n′; In the process of constructing the transfer V2G reallocation model, the transfer V2G reallocation model expression for each scheduling period t is: In the formula, min z represents the minimized total regional transfer time z; is the total section transfer time integral for the area; is the total intersection turn transfer time integral for the area; and are the transfer flows on the road segment (i, j) and the turn (i, j, l) respectively; and are the corresponding basic flows respectively; and are the transfer time impedances of (i, j) and (i, j, l), respectively, and are given by the model Γ BPR (·) and Γ WEB (·) calculated; Indicates that the kth path between the transfer origin-destination pair (m,n) passes through (i,j) or (i,j,l), otherwise it is 0; and are the transfer flow and time of the path respectively; q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; In the process of evaluating the on-time transfer V2G capability of setting the transfer duration by using the transfer V2G reallocation model, the on-time transfer V2G capability expression is: Where TC n is the on-time transfer V2G capability of the corresponding node n; q′ mn for l t The number of EVs that can reach n from m within q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; is the transfer time from m to n obtained by the V2G reallocation model; l t To limit the transfer time; E' mn is the corresponding EV set; e is the sequence number of EV; S min Minimum SOC threshold to prevent over-discharge; The remaining SOC of the e-th transferred EV after reaching destination n and participating in V2G; for Whether it complies with battery safety requirements, 1 or 0 represents compliance or non-compliance respectively; In the process of calculating the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index and obtaining the reduction rate of the transfer EV corresponding to the late transfer starting place, the expression of the transfer congestion index is: In the formula, θ mn is the transfer congestion index of m among all late origins of the dispatch destination n; Any transfer starting point node; for l t Can be transferred from any starting point node within The number of EVs reaching n; To transfer the V2G reallocation model from The transfer time to reach n; To shift the EV travel demand matrix The number of transferred EVs.

2. A transfer V2G capability assessment device based on adaptive reallocation, using the transfer V2G capability assessment method based on adaptive reallocation according to claim 1, characterized in that: include: A traffic network model construction and processing module is used to construct a traffic network model, determine the transfer instructions of the V2G site through the traffic network model, and match the transfer starting point with a number of V2G scheduling destinations to complete the transfer travel demand initialization; A just-in-time transfer V2G capability evaluation module is used to update the transfer travel demand matrix according to the matched transfer origin-dispatch destination pairs and the corresponding number of transfer EVs; construct a transfer V2G reallocation model, formulate travel routes and shortest travel times for transfer EVs through the transfer V2G reallocation model, and evaluate the just-in-time transfer V2G capability for a set transfer duration; A transfer EV scheduling quantity reduction module is used to calculate the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index, and obtain the reduction rate of the transfer EV corresponding to the late transfer starting place; according to the obtained transfer EV reduction rate, adaptively reduce the transfer EV scheduling quantity of the late transfer starting place, and update the transfer travel demand matrix; An iterative evaluation module for on-time transfer V2G capability is used to iteratively perform transfer V2G reallocation and evaluate the on-time transfer V2G capability through the transfer V2G reallocation model according to the updated transfer travel demand matrix until the set requirements are met, stop the iterative evaluation, and obtain an enhanced on-time transfer V2G capability; In the traffic network model construction and application module, the expression for completing the transfer travel demand initialization by matching the transfer starting point with a number of V2G scheduling destinations through the traffic network model is: In the formula, l t It is to limit the transfer time; N is the node set; are the traffic node sets that operate normally during period t and those that need additional V2G transfer; is the shortest travel time from m to n when only basic traffic conditions are considered; n′ is the dispatch destination node with the shortest travel time starting from m; s m is the number of EVs that can be dispatched at m; q mn′ is the updated transfer travel demand from m to n′; In the on-time transfer V2G capability evaluation module, in the process of constructing the transfer V2G reallocation model, the transfer V2G reallocation model expression for each scheduling period t is: In the formula, min z represents the minimized total regional transfer time z; is the total section transfer time integral for the area; is the total intersection turn transfer time integral for the area; and are the transfer flows on the road segment (i, j) and the turn (i, j, l) respectively; and are the corresponding basic flows respectively; and are the transfer time impedances of (i, j) and (i, j, l), respectively, and are given by the model Γ BPR (·) and Γ WEB (·) calculated; Indicates that the kth path between the transfer origin-destination pair (m,n) passes through (i,j) or (i,j,l), otherwise it is 0; and are the transfer flow and time of the path respectively; q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; In the on-time transfer V2G capability evaluation module, in the process of evaluating the on-time transfer V2G capability of setting the transfer duration through the transfer V2G reallocation model, the on-time transfer V2G capability expression is: Where TC n is the on-time transfer V2G capability of the corresponding node n; q′ mn for l t The number of EVs that can reach n from m within q mn is the number of transferred EVs in the (m,n) transfer EV travel demand matrix; is the transfer time from m to n obtained by the V2G reallocation model; l t To limit the transfer time; E' mn is the corresponding EV set; e is the sequence number of EV; S min Minimum SOC threshold to prevent over-discharge; The remaining SOC of the e-th transferred EV after reaching destination n and participating in V2G; for Whether it complies with battery safety requirements, 1 or 0 represents compliance or non-compliance respectively; In the transfer EV scheduling quantity reduction module, in the process of calculating the contribution or sensitivity of the late transfer starting place to the transfer congestion according to the transfer congestion index and obtaining the reduction rate of the transfer EV corresponding to the late transfer starting place, the expression of the transfer congestion index is: In the formula, θ mn is the transfer congestion index of m among all late origins of the dispatch destination n; Any transfer starting point node; for l t Can be transferred from any starting point node within The number of EVs reaching n; To transfer the V2G reallocation model from The transfer time to reach n; To shift the EV travel demand matrix The number of transferred EVs.

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